Reliability-based journey time prediction via two-stream deep learning with multi-source data

IF 2.8 3区 工程技术 Q3 TRANSPORTATION
Li Zhuang , Xinyue Wu , Andy H. F. Chow , Wei Ma , William H.K Lam , S. C. Wong
{"title":"Reliability-based journey time prediction via two-stream deep learning with multi-source data","authors":"Li Zhuang ,&nbsp;Xinyue Wu ,&nbsp;Andy H. F. Chow ,&nbsp;Wei Ma ,&nbsp;William H.K Lam ,&nbsp;S. C. Wong","doi":"10.1080/15472450.2023.2301707","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a distribution-free reliability-based prediction approach for estimating journey time intervals with multi-source data using a two-stream deep learning framework. The prediction framework consists of a long short-term memory (LSTM) module for extracting temporal features and a convolutional neural network (CNN) module for extracting spatial-temporal features from the heterogeneous data. The precision and reliability of the prediction are assessed respectively by the Mean Prediction Interval Width (MPIW) and Prediction Interval Coverage Probability (PICP) metrics. For computational effectiveness, a Gaussian approximation is adopted to formulate a smooth and differentiable loss function for training the prediction framework. The computational experiments are conducted based on a real-world Hong Kong corridor, where multi-source data including traffic and weather conditions are collected. The proposed framework shows significant improvements over existing methods in terms of both precision and reliability over a range of traffic and weather conditions. This study contributes to the development of reliability-based intelligent transportation systems with advanced deep learning techniques.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 134-152"},"PeriodicalIF":2.8000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Transportation Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1547245024000033","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0

Abstract

This paper presents a distribution-free reliability-based prediction approach for estimating journey time intervals with multi-source data using a two-stream deep learning framework. The prediction framework consists of a long short-term memory (LSTM) module for extracting temporal features and a convolutional neural network (CNN) module for extracting spatial-temporal features from the heterogeneous data. The precision and reliability of the prediction are assessed respectively by the Mean Prediction Interval Width (MPIW) and Prediction Interval Coverage Probability (PICP) metrics. For computational effectiveness, a Gaussian approximation is adopted to formulate a smooth and differentiable loss function for training the prediction framework. The computational experiments are conducted based on a real-world Hong Kong corridor, where multi-source data including traffic and weather conditions are collected. The proposed framework shows significant improvements over existing methods in terms of both precision and reliability over a range of traffic and weather conditions. This study contributes to the development of reliability-based intelligent transportation systems with advanced deep learning techniques.
通过多源数据的双流深度学习进行基于可靠性的行程时间预测
本文介绍了一种基于无分布可靠性的预测方法,该方法利用双流深度学习框架,通过多源数据估算行程时间间隔。预测...
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
8.80
自引率
19.40%
发文量
51
审稿时长
15 months
期刊介绍: The Journal of Intelligent Transportation Systems is devoted to scholarly research on the development, planning, management, operation and evaluation of intelligent transportation systems. Intelligent transportation systems are innovative solutions that address contemporary transportation problems. They are characterized by information, dynamic feedback and automation that allow people and goods to move efficiently. They encompass the full scope of information technologies used in transportation, including control, computation and communication, as well as the algorithms, databases, models and human interfaces. The emergence of these technologies as a new pathway for transportation is relatively new. The Journal of Intelligent Transportation Systems is especially interested in research that leads to improved planning and operation of the transportation system through the application of new technologies. The journal is particularly interested in research that adds to the scientific understanding of the impacts that intelligent transportation systems can have on accessibility, congestion, pollution, safety, security, noise, and energy and resource consumption. The journal is inter-disciplinary, and accepts work from fields of engineering, economics, planning, policy, business and management, as well as any other disciplines that contribute to the scientific understanding of intelligent transportation systems. The journal is also multi-modal, and accepts work on intelligent transportation for all forms of ground, air and water transportation. Example topics include the role of information systems in transportation, traffic flow and control, vehicle control, routing and scheduling, traveler response to dynamic information, planning for ITS innovations, evaluations of ITS field operational tests, ITS deployment experiences, automated highway systems, vehicle control systems, diffusion of ITS, and tools/software for analysis of ITS.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信